Machine Learning Methods to Predict Cancer Progression and Estimate Treatment Effectiveness

预测癌症进展和估计治疗效果的机器学习方法

基本信息

  • 批准号:
    10559507
  • 负责人:
  • 金额:
    $ 5.27万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-06-01 至 2027-02-28
  • 项目状态:
    未结题

项目摘要

Project Summary Cancer is a leading cause of death worldwide. In the past few years, an average of around 18.1 million new cases of cancer (per year) were diagnosed. Physicians often decide which treatment to give a patient with the goals of prolonging overall survival, preventing recurrence, and minimizing complications. Generally, Randomized Controlled Trials (RCTs) are used to determine the efficacy of one therapy versus another therapy but are untenable in many situations due to ethical and financial constraints. Recent work has leveraged observational data to develop machine learning models that capture the progression of chronic diseases such as Cystic Fibrosis and Parkinson’s. However, using machine learning to determine treatment efficacy and predict important clinical endpoints, such as overall survival (OS) or progression free survival (PFS), in cancer has not been well studied. This gap in knowledge is due to a lack of benchmark cancer datasets, limited sample sizes for rare cancers, and challenges specific to cancer management, such as tumor heterogeneity within patients leading to differential treatment response. In spite of these challenges, recent methodological improvements in machine learning, such as the use of inductive biases and auxiliary data to improve prediction in data-scarce settings as well as improved treatment effect estimation methods, present an opportunity to test the promise of machine learning in the cancer setting. Therefore, the overarching goal of the proposed work is to develop methods that will enable training of machine learning models that capture the signal in longitudinal, observational cancer data and ultimately improve prediction of clinical endpoints as well as estimation of cancer treatment effects. As a case study and evaluation bed for my development of these methods, I will focus on multiple myeloma, an incurable plasma cell cancer. Aim 1 of this proposal will focus on improving prediction of survival endpoints and depth of treatment response. I will train a latent variable model with a novel learning algorithm that will leverage auxiliary longitudinal data to improve the power of the model, enabling better prediction of clinical endpoints. Aim 2 will tackle the related, yet distinct, question of treatment effect estimation, particularly with respect to different combination chemotherapies. Meta-learner models will be used to estimate average and conditional average treatment effects. A sensitivity analysis framework with clinically-interpretable sensitive parameters will be used to assess reliability of the estimates. Finally, aim 3 will provide a machine learning decision support tool to augment physician decision making in cancer management. A user study will be conducted with the tool to determine if it improves physician assessment of patients. This proposal provides a general methodological framework that can be applied to any cancer dataset and improves understanding of how to effectively use machine learning models trained on observational data to improve care of cancer patients.
项目摘要 癌症是世界范围内主要的死亡原因。在过去的几年里,平均大约有1810万个新的 诊断癌症病例(每年)。医生通常会决定给患有 目标是延长总生存期,防止复发,并将并发症降至最低。一般来说, 随机对照试验(RCT)用于确定一种疗法与另一种疗法的疗效 但在许多情况下,由于道德和财政限制,这是站不住脚的。最近的工作利用了 用于开发机器学习模型的观测数据,以捕获慢性病的进展情况 如囊性纤维化和帕金森氏症。然而,使用机器学习来确定治疗效果和 预测癌症的重要临床终点,如总生存期(OS)或无进展生存期(PFS) 还没有得到很好的研究。这一知识差距是由于缺乏基准癌症数据集,有限 罕见癌症的样本大小,以及肿瘤异质性等癌症管理特有的挑战 在患者内部导致差异化治疗反应。尽管存在这些挑战,最近的方法论 机器学习方面的改进,例如使用归纳偏差和辅助数据来改进预测 在数据稀缺的情况下以及改进的治疗效果评估方法中,提供了测试的机会 机器学习在癌症环境中的前景。因此,拟议的总体目标是 工作是开发能够训练捕获信号的机器学习模型的方法 在纵向的、观察性的癌症数据中,并最终改善临床终点的预测 以及癌症治疗效果的评估。作为我发展的案例研究和评估平台 在这些方法中,我将重点介绍多发性骨髓瘤,一种无法治愈的浆细胞癌。本提案的目标1将 重点提高对生存终点和治疗反应深度的预测。我会训练一个潜伏者 可变模型,具有一种新的学习算法,将利用辅助纵向数据来改进 模型的强大功能,能够更好地预测临床终点。目标2将解决相关但又不同的 治疗效果评估问题,特别是关于不同联合化疗的问题。 元学习者模型将被用来估计平均和条件平均治疗效果。一种敏感 将使用具有临床可解释的敏感参数的分析框架来评估 估计。最后,Aim 3将提供一个机器学习决策支持工具来增强医生的决策 在癌症管理方面取得的成就。将使用该工具进行用户研究,以确定其是否有所改善 医生对病人的评估。这项建议提供了一个通用的方法论框架,可以 应用于任何癌症数据集,并提高了对如何有效使用机器学习模型的理解 对观察数据进行培训,以改善对癌症患者的护理。

项目成果

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Zeshan M Hussain其他文献

Zeshan M Hussain的其他文献

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{{ truncateString('Zeshan M Hussain', 18)}}的其他基金

Machine Learning Methods to Predict Cancer Progression and Estimate Treatment Effectiveness
预测癌症进展和估计治疗效果的机器学习方法
  • 批准号:
    10384213
  • 财政年份:
    2022
  • 资助金额:
    $ 5.27万
  • 项目类别:

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